Design Of Handheld Raman System And Application For Rapid Identification Of Seed Varieties

Raman spectroscopy is a technology that analyzes the structure and composition of sample by detecting changes in photon energy and frequency resulting from the inter-action between light and material.Due to its non-invasive detection characteristics,Ra-man spectroscopy is widely used for detection in various fields,and its application sce-narios continue to expand.Hand-held Raman spectrometers have garnered widespread attention because traditional spectrometers cannot perform real-time measurements in special environments,such as outdoors,high temperatures,and high pressures.Seed classification and identification are crucial aspects of agricultural production and breed-ing.By classifying and screening seeds based on their characteristics,crop yield and quality can be significantly improved in the field of agriculture.Hand-held Raman spectrometers,due to their portability and high efficiency,play an important role in seed screening.Despite some domestic companies launching handheld Raman equip-ment,the market for such devices used in agriculture is primarily dominated by foreign companies,placing domestic manufacturers at a disadvantage.Furthermore,the perfor-mance and stability of domestically produced equipment still require improvement.So designing a low-cost,stable,and hand-held Raman spectrometer equipped with identi-fication algorithms would be of great significance.This work presents a comprehensive overview of the development of hand-held Raman equipment,as well as an in-depth study of the principle of Raman scattering.Next,appropriate lasers,miniature spectrometers,probes,network cards,and control circuit modules were selected based on their intended usage scenarios.A software de-velopment environment was established,followed by the design of the human-computer interaction interface to meet the application requirements.The hardware and software design of the hand-held Raman spectrometer was then completed,and overall testing was conducted using alfalfa seeds as the measurement sample.In the second part of the study,we compared the Raman spectra of four types of al-falfa and two types of sweet-scented clover seeds.Significant differences in the intensity of alfalfa and sweet-scented clover seeds were observed at 524 cm^-1,1140-1300 cm^-1,and 1500-1700 cm^-1,which could be attributed to differences in their nutritional com-position such as sugar and protein content.After processing the collected 493 effective Raman spectra with dimensionality reduction methods and machine classification algo-rithms,we identified six types of seeds.We compared the recognition capabilities of 28 classification models and found that the model combining principal component analy-sis and linear discriminant analysis performed well.The accuracy rate of the classifier combined with linear discriminant analysis was over 94%,with the k-nearest neighbor algorithm achieving the highest accuracy rate of 98.88%.These results suggest that the combination of Raman spectroscopy and machine learning has great potential in the rapid analysis and identification of seeds.This work can promote the development of domestic handheld Raman equipment and provide more accurate and reliable technical support for the seed industry.